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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Simple Regression Y ~ X.wasp
Title produced by softwareSimple Linear Regression
Date of computationSat, 01 Nov 2014 12:06:59 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/01/t1414843647674t67zdwwz0knm.htm/, Retrieved Sun, 19 May 2024 12:57:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=250671, Retrieved Sun, 19 May 2024 12:57:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [chi 1] [2014-11-01 11:11:13] [55a850ac261e4a7d4f206113c00d6f60]
- RMPD    [Simple Linear Regression] [chi 3] [2014-11-01 12:06:59] [0015a2406d94cac8c1a56a29b9122359] [Current]
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Dataseries X:
87.28	255
87.28	280.2
87.09	299.9
86.92	339.2
87.59	374.2
90.72	393.5
90.69	389.2
90.3	381.7
89.55	375.2
88.94	369
88.41	357.4
87.82	352.1
87.07	346.5
86.82	342.9
86.4	340.3
86.02	328.3
85.66	322.9
85.32	314.3
85	308.9
84.67	294
83.94	285.6
82.83	281.2
81.95	280.3
81.19	278.8
80.48	274.5
78.86	270.4
69.47	263.4
68.77	259.9
70.06	258
73.95	262.7
75.8	284.7
77.79	311.3
81.57	322.1
83.07	327
84.34	331.3
85.1	333.3
85.25	321.4
84.26	327
83.63	320
86.44	314.7
85.3	316.7
84.1	314.4
83.36	321.3
82.48	318.2
81.58	307.2
80.47	301.3
79.34	287.5
82.13	277.7
81.69	274.4
80.7	258.8
79.88	253.3
79.16	251
78.38	248.4
77.42	249.5
76.47	246.1
75.46	244.5
74.48	243.6
78.27	244
80.7	240.8
79.91	249.8
78.75	248
77.78	259.4
81.14	260.5
81.08	260.8
80.03	261.3
78.91	259.5
78.01	256.6
76.9	257.9
75.97	256.5
81.93	254.2
80.27	253.3
78.67	253.8
77.42	255.5
76.16	257.1
74.7	257.3
76.39	253.2
76.04	252.8
74.65	252
73.29	250.7
71.79	252.2
74.39	250
74.91	251
74.54	253.4
73.08	251.2
72.75	255.6
71.32	261.1
70.38	258.9
70.35	259.9
70.01	261.2
69.36	264.7
67.77	267.1
69.26	266.4
69.8	267.7
68.38	268.6
67.62	267.5
68.39	268.5
66.95	268.5
65.21	270.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=250671&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=250671&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=250671&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)-67.87334.091-1.9910.049
X4.4560.42910.3890
- - -
Residual Std. Err. 26.999 on 96 df
Multiple R-sq. 0.529
Adjusted R-sq. 0.524

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
(Intercept) & -67.873 & 34.091 & -1.991 & 0.049 \tabularnewline
X & 4.456 & 0.429 & 10.389 & 0 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 26.999  on  96 df \tabularnewline
Multiple R-sq.  & 0.529 \tabularnewline
Adjusted R-sq.  & 0.524 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=250671&T=1

[TABLE]
[ROW][C]Linear Regression Model[/C][/ROW]
[ROW][C]Y ~ X[/C][/ROW]
[ROW][C]coefficients:[/C][C] [/C][/ROW]
[ROW][C] [/C][C]Estimate[/C][C]Std. Error[/C][C]t value[/C][C]Pr(>|t|)[/C][/ROW]
[C](Intercept)[/C][C]-67.873[/C][C]34.091[/C][C]-1.991[/C][C]0.049[/C][/ROW]
[C]X[/C][C]4.456[/C][C]0.429[/C][C]10.389[/C][C]0[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]26.999  on  96 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0.529[/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]0.524[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=250671&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=250671&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Linear Regression Model
Y ~ X
coefficients:
EstimateStd. Errort valuePr(>|t|)
(Intercept)-67.87334.091-1.9910.049
X4.4560.42910.3890
- - -
Residual Std. Err. 26.999 on 96 df
Multiple R-sq. 0.529
Adjusted R-sq. 0.524







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
X178673.87578673.875107.9270
Residuals9669979.787728.956

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
X & 1 & 78673.875 & 78673.875 & 107.927 & 0 \tabularnewline
Residuals & 96 & 69979.787 & 728.956 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=250671&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]X[/C][C]1[/C][C]78673.875[/C][C]78673.875[/C][C]107.927[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]96[/C][C]69979.787[/C][C]728.956[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=250671&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=250671&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
X178673.87578673.875107.9270
Residuals9669979.787728.956



Parameters (Session):
par1 = 2 ; par2 = 1 ; par3 = TRUE ;
Parameters (R input):
par1 = 2 ; par2 = 1 ; par3 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1)
cat2<- as.numeric(par2)
intercept<-as.logical(par3)
x <- t(x)
xdf<-data.frame(t(y))
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
xdf <- data.frame(xdf[[cat1]], xdf[[cat2]])
names(xdf)<-c('Y', 'X')
if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) )
sumlmxdf<-summary(lmxdf)
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
nc <- ncol(sumlmxdf$'coefficients')
nr <- nrow(sumlmxdf$'coefficients')
a<-table.row.start(a)
a<-table.element(a,'Linear Regression Model', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],nc+1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'coefficients:',1,TRUE)
a<-table.element(a, ' ',nc,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
for(i in 1 : nc){
a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE)
}#end header
a<-table.row.end(a)
for(i in 1: nr){
a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE)
for(j in 1 : nc){
a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE)
}
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, '- - - ',1,TRUE)
a<-table.element(a, ' ',nc,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Std. Err. ',1,TRUE)
a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
a<-table.element(a, 'Df',1,TRUE)
a<-table.element(a, 'Sum Sq',1,TRUE)
a<-table.element(a, 'Mean Sq',1,TRUE)
a<-table.element(a, 'F value',1,TRUE)
a<-table.element(a, 'Pr(>F)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,1,TRUE)
a<-table.element(a, anova.xdf$Df[1])
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',1,TRUE)
a<-table.element(a, anova.xdf$Df[2])
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3))
a<-table.element(a, ' ')
a<-table.element(a, ' ')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='regressionplot.png')
plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution')
if(intercept == TRUE) abline(coef(lmxdf), col='red')
if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red')
dev.off()
library(car)
bitmap(file='residualsQQplot.png')
qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit')
dev.off()
bitmap(file='residualsplot.png')
plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit')
dev.off()
bitmap(file='cooksDistanceLmplot.png')
plot.lm(lmxdf, which=4)
dev.off()